{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 《零基础财务Python训练营》实操练习题\n",
    "## Day5 《数据处理和分析（下）》"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**作业说明:**  为了帮助同学们掌握、巩固课程学习内容，并主动结合知识点思考问题逻辑，从而形成编程思维，我们为大家设计各知识点对应的作业\n",
    "\n",
    "▶本章内容均为基础题，是针对当前章节传授知识点设计的题目，用于考察大家的学习掌握情况，同学们一定要尝试做出来哟~\n",
    "\n",
    "★对于以上说明和作业问题，同学们可以在群里积极参与讨论或向助教老师咨询呦，相信大家只要认真完成作业，一定能够收获满满！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【基础题】 金融机构营销活动分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.1 知识点回顾\n",
    "<br>结合数据探索知识简单查看、预览数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）在python中读取作业中的csv文件，DataFrame的变量名要求为\"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>26</td>\n",
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       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
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       "      <td>10002</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>829.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
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       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>769.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>46</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1199.0</td>\n",
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       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>10073</th>\n",
       "      <td>20072</td>\n",
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       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>10074</th>\n",
       "      <td>20073</td>\n",
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       "      <td>no</td>\n",
       "      <td>733.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>16.0</td>\n",
       "      <td>jun</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>10075</th>\n",
       "      <td>20074</td>\n",
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       "      <td>technician</td>\n",
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       "      <td>no</td>\n",
       "      <td>29.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19.0</td>\n",
       "      <td>aug</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>10076</th>\n",
       "      <td>20075</td>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
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       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8.0</td>\n",
       "      <td>may</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10077</th>\n",
       "      <td>20076</td>\n",
       "      <td>34</td>\n",
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       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>9.0</td>\n",
       "      <td>jul</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>no</td>\n",
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       "</table>\n",
       "<p>10078 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  age          job   marital  education default  balance housing  \\\n",
       "0      10001   26   unemployed    single   tertiary      no    814.0      no   \n",
       "1      10002   49   technician   married  secondary      no    808.0     yes   \n",
       "2      10003   34      unknown    single  secondary      no    859.0      no   \n",
       "3      10004   28      unknown    single  secondary      no   4465.0      no   \n",
       "4      10005   46   technician  divorced   tertiary      no      0.0      no   \n",
       "...      ...  ...          ...       ...        ...     ...      ...     ...   \n",
       "10073  20072   33  blue-collar    single    primary      no      1.0     yes   \n",
       "10074  20073   39     services   married  secondary      no    733.0      no   \n",
       "10075  20074   32   technician    single  secondary      no     29.0      no   \n",
       "10076  20075   43   technician   married  secondary      no      0.0      no   \n",
       "10077  20076   34   technician   married  secondary      no      0.0      no   \n",
       "\n",
       "      loan   contact   day month  duration  campaign  pdays  previous  \\\n",
       "0       no  cellular  28.0   jan    1387.0       1.0   -1.0       0.0   \n",
       "1       no  cellular  28.0   jan    1232.0       1.0   -1.0       0.0   \n",
       "2       no  cellular  28.0   jan     829.0       1.0   -1.0       0.0   \n",
       "3       no  cellular  28.0   jan     769.0       1.0   -1.0       0.0   \n",
       "4       no  cellular  28.0   jan    1199.0       2.0   -1.0       0.0   \n",
       "...    ...       ...   ...   ...       ...       ...    ...       ...   \n",
       "10073   no  cellular  20.0   apr     257.0       1.0   -1.0       0.0   \n",
       "10074   no   unknown  16.0   jun      83.0       4.0   -1.0       0.0   \n",
       "10075   no  cellular  19.0   aug     156.0       2.0   -1.0       0.0   \n",
       "10076  yes  cellular   8.0   may       9.0       2.0  172.0       5.0   \n",
       "10077   no  cellular   9.0   jul     628.0       1.0   -1.0       0.0   \n",
       "\n",
       "      poutcome deposit  \n",
       "0      unknown     yes  \n",
       "1      unknown     yes  \n",
       "2      unknown     yes  \n",
       "3      unknown     yes  \n",
       "4      unknown     yes  \n",
       "...        ...     ...  \n",
       "10073  unknown      no  \n",
       "10074  unknown      no  \n",
       "10075  unknown      no  \n",
       "10076  failure      no  \n",
       "10077  unknown      no  \n",
       "\n",
       "[10078 rows x 18 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('./bank.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）使用head，shape，info，describle()函数查看数据情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>no</td>\n",
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       "      <td>20076</td>\n",
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      "text/plain": [
       "          id  age          job  marital  education default  balance housing  \\\n",
       "10073  20072   33  blue-collar   single    primary      no      1.0     yes   \n",
       "10074  20073   39     services  married  secondary      no    733.0      no   \n",
       "10075  20074   32   technician   single  secondary      no     29.0      no   \n",
       "10076  20075   43   technician  married  secondary      no      0.0      no   \n",
       "10077  20076   34   technician  married  secondary      no      0.0      no   \n",
       "\n",
       "      loan   contact   day month  duration  campaign  pdays  previous  \\\n",
       "10073   no  cellular  20.0   apr     257.0       1.0   -1.0       0.0   \n",
       "10074   no   unknown  16.0   jun      83.0       4.0   -1.0       0.0   \n",
       "10075   no  cellular  19.0   aug     156.0       2.0   -1.0       0.0   \n",
       "10076  yes  cellular   8.0   may       9.0       2.0  172.0       5.0   \n",
       "10077   no  cellular   9.0   jul     628.0       1.0   -1.0       0.0   \n",
       "\n",
       "      poutcome deposit  \n",
       "10073  unknown      no  \n",
       "10074  unknown      no  \n",
       "10075  unknown      no  \n",
       "10076  failure      no  \n",
       "10077  unknown      no  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.head(10)\n",
    "\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10078, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10078 entries, 0 to 10077\n",
      "Data columns (total 18 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   id         10078 non-null  int64  \n",
      " 1   age        10078 non-null  int64  \n",
      " 2   job        10078 non-null  object \n",
      " 3   marital    10076 non-null  object \n",
      " 4   education  10077 non-null  object \n",
      " 5   default    10073 non-null  object \n",
      " 6   balance    10075 non-null  float64\n",
      " 7   housing    10071 non-null  object \n",
      " 8   loan       10069 non-null  object \n",
      " 9   contact    10068 non-null  object \n",
      " 10  day        10074 non-null  float64\n",
      " 11  month      10069 non-null  object \n",
      " 12  duration   10076 non-null  float64\n",
      " 13  campaign   10075 non-null  float64\n",
      " 14  pdays      10076 non-null  float64\n",
      " 15  previous   10075 non-null  float64\n",
      " 16  poutcome   10072 non-null  object \n",
      " 17  deposit    10076 non-null  object \n",
      "dtypes: float64(6), int64(2), object(10)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10078.000000</td>\n",
       "      <td>10078.000000</td>\n",
       "      <td>10075.000000</td>\n",
       "      <td>10074.000000</td>\n",
       "      <td>10076.000000</td>\n",
       "      <td>10075.000000</td>\n",
       "      <td>10076.000000</td>\n",
       "      <td>10075.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15038.032149</td>\n",
       "      <td>41.321095</td>\n",
       "      <td>1537.467395</td>\n",
       "      <td>15.512011</td>\n",
       "      <td>321.854407</td>\n",
       "      <td>2.466104</td>\n",
       "      <td>56.674573</td>\n",
       "      <td>0.918313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2908.895780</td>\n",
       "      <td>12.127400</td>\n",
       "      <td>3286.644262</td>\n",
       "      <td>8.478347</td>\n",
       "      <td>298.012987</td>\n",
       "      <td>2.727866</td>\n",
       "      <td>113.033388</td>\n",
       "      <td>2.395478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10001.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>-6847.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12519.250000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15037.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17556.750000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>89.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20076.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>81204.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3284.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id           age       balance           day      duration  \\\n",
       "count  10078.000000  10078.000000  10075.000000  10074.000000  10076.000000   \n",
       "mean   15038.032149     41.321095   1537.467395     15.512011    321.854407   \n",
       "std     2908.895780     12.127400   3286.644262      8.478347    298.012987   \n",
       "min    10001.000000     18.000000  -6847.000000      1.000000      2.000000   \n",
       "25%    12519.250000     32.000000    126.000000      8.000000    130.000000   \n",
       "50%    15037.500000     39.000000    550.000000     15.000000    231.000000   \n",
       "75%    17556.750000     49.000000   1708.000000     22.000000    404.000000   \n",
       "max    20076.000000     95.000000  81204.000000     31.000000   3284.000000   \n",
       "\n",
       "           campaign         pdays      previous  \n",
       "count  10075.000000  10076.000000  10075.000000  \n",
       "mean       2.466104     56.674573      0.918313  \n",
       "std        2.727866    113.033388      2.395478  \n",
       "min        1.000000     -1.000000      0.000000  \n",
       "25%        1.000000     -1.000000      0.000000  \n",
       "50%        2.000000     -1.000000      0.000000  \n",
       "75%        3.000000     89.250000      1.000000  \n",
       "max       63.000000    854.000000     58.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=#A64646 size=2> **看来同学们已经掌握了数据探索秘诀啦！**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.2 数据初步分析\n",
    "<br>进一步探索数据奥秘"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）对数据进行预处理，删除空值和重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10017 entries, 0 to 10077\n",
      "Data columns (total 18 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   id         10017 non-null  int64  \n",
      " 1   age        10017 non-null  int64  \n",
      " 2   job        10017 non-null  object \n",
      " 3   marital    10017 non-null  object \n",
      " 4   education  10017 non-null  object \n",
      " 5   default    10017 non-null  object \n",
      " 6   balance    10017 non-null  float64\n",
      " 7   housing    10017 non-null  object \n",
      " 8   loan       10017 non-null  object \n",
      " 9   contact    10017 non-null  object \n",
      " 10  day        10017 non-null  float64\n",
      " 11  month      10017 non-null  object \n",
      " 12  duration   10017 non-null  float64\n",
      " 13  campaign   10017 non-null  float64\n",
      " 14  pdays      10017 non-null  float64\n",
      " 15  previous   10017 non-null  float64\n",
      " 16  poutcome   10017 non-null  object \n",
      " 17  deposit    10017 non-null  object \n",
      "dtypes: float64(6), int64(2), object(10)\n",
      "memory usage: 1.5+ MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9999 entries, 0 to 10077\n",
      "Data columns (total 18 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   id         9999 non-null   int64  \n",
      " 1   age        9999 non-null   int64  \n",
      " 2   job        9999 non-null   object \n",
      " 3   marital    9999 non-null   object \n",
      " 4   education  9999 non-null   object \n",
      " 5   default    9999 non-null   object \n",
      " 6   balance    9999 non-null   float64\n",
      " 7   housing    9999 non-null   object \n",
      " 8   loan       9999 non-null   object \n",
      " 9   contact    9999 non-null   object \n",
      " 10  day        9999 non-null   float64\n",
      " 11  month      9999 non-null   object \n",
      " 12  duration   9999 non-null   float64\n",
      " 13  campaign   9999 non-null   float64\n",
      " 14  pdays      9999 non-null   float64\n",
      " 15  previous   9999 non-null   float64\n",
      " 16  poutcome   9999 non-null   object \n",
      " 17  deposit    9999 non-null   object \n",
      "dtypes: float64(6), int64(2), object(10)\n",
      "memory usage: 1.4+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(9999, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.dropna(inplace=True)\n",
    "data.info()\n",
    "\n",
    "data.drop_duplicates(inplace=True)\n",
    "data.info()\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）请同学根据下列表格用rename重置列名"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>26</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>814.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>49</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>808.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>34</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>859.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>829.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>28</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>769.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>46</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  age         job   marital  education default  balance housing loan  \\\n",
       "0  10001   26  unemployed    single   tertiary      no    814.0      no   no   \n",
       "1  10002   49  technician   married  secondary      no    808.0     yes   no   \n",
       "2  10003   34     unknown    single  secondary      no    859.0      no   no   \n",
       "3  10004   28     unknown    single  secondary      no   4465.0      no   no   \n",
       "4  10005   46  technician  divorced   tertiary      no      0.0      no   no   \n",
       "\n",
       "    contact   day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0  cellular  28.0   jan    1387.0       1.0   -1.0       0.0  unknown     yes  \n",
       "1  cellular  28.0   jan    1232.0       1.0   -1.0       0.0  unknown     yes  \n",
       "2  cellular  28.0   jan     829.0       1.0   -1.0       0.0  unknown     yes  \n",
       "3  cellular  28.0   jan     769.0       1.0   -1.0       0.0  unknown     yes  \n",
       "4  cellular  28.0   jan    1199.0       2.0   -1.0       0.0  unknown     yes  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>编号</th>\n",
       "      <th>年龄</th>\n",
       "      <th>职业</th>\n",
       "      <th>婚姻状况</th>\n",
       "      <th>受教育程度</th>\n",
       "      <th>是否有违约记录</th>\n",
       "      <th>每年账户平均余额</th>\n",
       "      <th>是否有住房贷款</th>\n",
       "      <th>是否有个人贷款</th>\n",
       "      <th>联系途径</th>\n",
       "      <th>最后一次联系时间（日）</th>\n",
       "      <th>最后一次联系时间（月）</th>\n",
       "      <th>最后一次联系交流时长</th>\n",
       "      <th>与该客户交流过的次数</th>\n",
       "      <th>距离上次联系客户后过了多久</th>\n",
       "      <th>之前与客户交流过的次数</th>\n",
       "      <th>上次活动的结果</th>\n",
       "      <th>预测客户是否会订购定期存款业务</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>26</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>814.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>49</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>808.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>34</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>859.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>829.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>28</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>769.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10005</td>\n",
       "      <td>46</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10073</th>\n",
       "      <td>20072</td>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>20.0</td>\n",
       "      <td>apr</td>\n",
       "      <td>257.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10074</th>\n",
       "      <td>20073</td>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>733.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>16.0</td>\n",
       "      <td>jun</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10075</th>\n",
       "      <td>20074</td>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19.0</td>\n",
       "      <td>aug</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10076</th>\n",
       "      <td>20075</td>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8.0</td>\n",
       "      <td>may</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10077</th>\n",
       "      <td>20076</td>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>9.0</td>\n",
       "      <td>jul</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          编号  年龄           职业      婚姻状况      受教育程度 是否有违约记录  每年账户平均余额 是否有住房贷款  \\\n",
       "0      10001  26   unemployed    single   tertiary      no     814.0      no   \n",
       "1      10002  49   technician   married  secondary      no     808.0     yes   \n",
       "2      10003  34      unknown    single  secondary      no     859.0      no   \n",
       "3      10004  28      unknown    single  secondary      no    4465.0      no   \n",
       "4      10005  46   technician  divorced   tertiary      no       0.0      no   \n",
       "...      ...  ..          ...       ...        ...     ...       ...     ...   \n",
       "10073  20072  33  blue-collar    single    primary      no       1.0     yes   \n",
       "10074  20073  39     services   married  secondary      no     733.0      no   \n",
       "10075  20074  32   technician    single  secondary      no      29.0      no   \n",
       "10076  20075  43   technician   married  secondary      no       0.0      no   \n",
       "10077  20076  34   technician   married  secondary      no       0.0      no   \n",
       "\n",
       "      是否有个人贷款      联系途径  最后一次联系时间（日） 最后一次联系时间（月）  最后一次联系交流时长  与该客户交流过的次数  \\\n",
       "0          no  cellular         28.0         jan      1387.0         1.0   \n",
       "1          no  cellular         28.0         jan      1232.0         1.0   \n",
       "2          no  cellular         28.0         jan       829.0         1.0   \n",
       "3          no  cellular         28.0         jan       769.0         1.0   \n",
       "4          no  cellular         28.0         jan      1199.0         2.0   \n",
       "...       ...       ...          ...         ...         ...         ...   \n",
       "10073      no  cellular         20.0         apr       257.0         1.0   \n",
       "10074      no   unknown         16.0         jun        83.0         4.0   \n",
       "10075      no  cellular         19.0         aug       156.0         2.0   \n",
       "10076     yes  cellular          8.0         may         9.0         2.0   \n",
       "10077      no  cellular          9.0         jul       628.0         1.0   \n",
       "\n",
       "       距离上次联系客户后过了多久  之前与客户交流过的次数  上次活动的结果 预测客户是否会订购定期存款业务  \n",
       "0               -1.0          0.0  unknown             yes  \n",
       "1               -1.0          0.0  unknown             yes  \n",
       "2               -1.0          0.0  unknown             yes  \n",
       "3               -1.0          0.0  unknown             yes  \n",
       "4               -1.0          0.0  unknown             yes  \n",
       "...              ...          ...      ...             ...  \n",
       "10073           -1.0          0.0  unknown              no  \n",
       "10074           -1.0          0.0  unknown              no  \n",
       "10075           -1.0          0.0  unknown              no  \n",
       "10076          172.0          5.0  failure              no  \n",
       "10077           -1.0          0.0  unknown              no  \n",
       "\n",
       "[9999 rows x 18 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.rename(columns={\n",
    "    'id': '编号',\n",
    "    'age': '年龄',\n",
    "    'job':'职业',\n",
    "    'marital':'婚姻状况',\n",
    "    'education':'受教育程度',\n",
    "    'default':'是否有违约记录',\n",
    "    'balance':'每年账户平均余额',\n",
    "    'housing':'是否有住房贷款',\n",
    "    'loan':'是否有个人贷款',\n",
    "    'contact':'联系途径',\n",
    "    'day':'最后一次联系时间（日）',\n",
    "    'month':'最后一次联系时间（月）',\n",
    "    'duration':'最后一次联系交流时长',\n",
    "    'campaign':'与该客户交流过的次数',\n",
    "    'pdays':'距离上次联系客户后过了多久',\n",
    "    'previous':'之前与客户交流过的次数',\n",
    "    'poutcome':'上次活动的结果',\n",
    "    'deposit':'预测客户是否会订购定期存款业务'\n",
    "}, inplace=True)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（3）将新数据表中的“编号”设置为新的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.set_index('编号', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年龄</th>\n",
       "      <th>职业</th>\n",
       "      <th>婚姻状况</th>\n",
       "      <th>受教育程度</th>\n",
       "      <th>是否有违约记录</th>\n",
       "      <th>每年账户平均余额</th>\n",
       "      <th>是否有住房贷款</th>\n",
       "      <th>是否有个人贷款</th>\n",
       "      <th>联系途径</th>\n",
       "      <th>最后一次联系时间（日）</th>\n",
       "      <th>最后一次联系时间（月）</th>\n",
       "      <th>最后一次联系交流时长</th>\n",
       "      <th>与该客户交流过的次数</th>\n",
       "      <th>距离上次联系客户后过了多久</th>\n",
       "      <th>之前与客户交流过的次数</th>\n",
       "      <th>上次活动的结果</th>\n",
       "      <th>预测客户是否会订购定期存款业务</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10001</th>\n",
       "      <td>26</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>814.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>49</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>808.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10003</th>\n",
       "      <td>34</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>859.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>829.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10004</th>\n",
       "      <td>28</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4465.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>769.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10005</th>\n",
       "      <td>46</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28.0</td>\n",
       "      <td>jan</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20072</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>20.0</td>\n",
       "      <td>apr</td>\n",
       "      <td>257.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20073</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>733.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>16.0</td>\n",
       "      <td>jun</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20074</th>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19.0</td>\n",
       "      <td>aug</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20075</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8.0</td>\n",
       "      <td>may</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>9.0</td>\n",
       "      <td>jul</td>\n",
       "      <td>628.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       年龄           职业      婚姻状况      受教育程度 是否有违约记录  每年账户平均余额 是否有住房贷款 是否有个人贷款  \\\n",
       "编号                                                                              \n",
       "10001  26   unemployed    single   tertiary      no     814.0      no      no   \n",
       "10002  49   technician   married  secondary      no     808.0     yes      no   \n",
       "10003  34      unknown    single  secondary      no     859.0      no      no   \n",
       "10004  28      unknown    single  secondary      no    4465.0      no      no   \n",
       "10005  46   technician  divorced   tertiary      no       0.0      no      no   \n",
       "...    ..          ...       ...        ...     ...       ...     ...     ...   \n",
       "20072  33  blue-collar    single    primary      no       1.0     yes      no   \n",
       "20073  39     services   married  secondary      no     733.0      no      no   \n",
       "20074  32   technician    single  secondary      no      29.0      no      no   \n",
       "20075  43   technician   married  secondary      no       0.0      no     yes   \n",
       "20076  34   technician   married  secondary      no       0.0      no      no   \n",
       "\n",
       "           联系途径  最后一次联系时间（日） 最后一次联系时间（月）  最后一次联系交流时长  与该客户交流过的次数  \\\n",
       "编号                                                                 \n",
       "10001  cellular         28.0         jan      1387.0         1.0   \n",
       "10002  cellular         28.0         jan      1232.0         1.0   \n",
       "10003  cellular         28.0         jan       829.0         1.0   \n",
       "10004  cellular         28.0         jan       769.0         1.0   \n",
       "10005  cellular         28.0         jan      1199.0         2.0   \n",
       "...         ...          ...         ...         ...         ...   \n",
       "20072  cellular         20.0         apr       257.0         1.0   \n",
       "20073   unknown         16.0         jun        83.0         4.0   \n",
       "20074  cellular         19.0         aug       156.0         2.0   \n",
       "20075  cellular          8.0         may         9.0         2.0   \n",
       "20076  cellular          9.0         jul       628.0         1.0   \n",
       "\n",
       "       距离上次联系客户后过了多久  之前与客户交流过的次数  上次活动的结果 预测客户是否会订购定期存款业务  \n",
       "编号                                                          \n",
       "10001           -1.0          0.0  unknown             yes  \n",
       "10002           -1.0          0.0  unknown             yes  \n",
       "10003           -1.0          0.0  unknown             yes  \n",
       "10004           -1.0          0.0  unknown             yes  \n",
       "10005           -1.0          0.0  unknown             yes  \n",
       "...              ...          ...      ...             ...  \n",
       "20072           -1.0          0.0  unknown              no  \n",
       "20073           -1.0          0.0  unknown              no  \n",
       "20074           -1.0          0.0  unknown              no  \n",
       "20075          172.0          5.0  failure              no  \n",
       "20076           -1.0          0.0  unknown              no  \n",
       "\n",
       "[9999 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（4）请同学用group_by函数对“受教育程度”进行分组，并查看各分组的人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "受教育程度\n",
       "primary      1338\n",
       "secondary    4879\n",
       "tertiary     3319\n",
       "unknown       463\n",
       "Name: 年龄, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.groupby('受教育程度')['年龄'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（5）请同学用group_by函数对“受教育程度”进行分组，并查看各分组的平均年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "受教育程度\n",
       "primary      48.654709\n",
       "secondary    40.143267\n",
       "tertiary     39.522145\n",
       "unknown      45.498920\n",
       "Name: 年龄, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.groupby('受教育程度')['年龄'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（6）请同学使用sort_values函数，按照客户'每年账户平均余额'进行降序排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年龄</th>\n",
       "      <th>职业</th>\n",
       "      <th>婚姻状况</th>\n",
       "      <th>受教育程度</th>\n",
       "      <th>是否有违约记录</th>\n",
       "      <th>每年账户平均余额</th>\n",
       "      <th>是否有住房贷款</th>\n",
       "      <th>是否有个人贷款</th>\n",
       "      <th>联系途径</th>\n",
       "      <th>最后一次联系时间（日）</th>\n",
       "      <th>最后一次联系时间（月）</th>\n",
       "      <th>最后一次联系交流时长</th>\n",
       "      <th>与该客户交流过的次数</th>\n",
       "      <th>距离上次联系客户后过了多久</th>\n",
       "      <th>之前与客户交流过的次数</th>\n",
       "      <th>上次活动的结果</th>\n",
       "      <th>预测客户是否会订购定期存款业务</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11955</th>\n",
       "      <td>84</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>81204.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>28.0</td>\n",
       "      <td>dec</td>\n",
       "      <td>679.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>other</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12292</th>\n",
       "      <td>84</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>81204.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>1.0</td>\n",
       "      <td>apr</td>\n",
       "      <td>390.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17123</th>\n",
       "      <td>52</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>66653.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14.0</td>\n",
       "      <td>aug</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19058</th>\n",
       "      <td>43</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>56831.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>15.0</td>\n",
       "      <td>may</td>\n",
       "      <td>243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12148</th>\n",
       "      <td>61</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>52587.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15.0</td>\n",
       "      <td>feb</td>\n",
       "      <td>394.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14925</th>\n",
       "      <td>49</td>\n",
       "      <td>management</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>-2049.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>30.0</td>\n",
       "      <td>may</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15303</th>\n",
       "      <td>51</td>\n",
       "      <td>management</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>-2282.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14.0</td>\n",
       "      <td>jul</td>\n",
       "      <td>301.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19557</th>\n",
       "      <td>52</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>-2712.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>2.0</td>\n",
       "      <td>apr</td>\n",
       "      <td>253.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10437</th>\n",
       "      <td>39</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>-3058.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17.0</td>\n",
       "      <td>apr</td>\n",
       "      <td>882.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15712</th>\n",
       "      <td>49</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6847.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>21.0</td>\n",
       "      <td>jul</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       年龄             职业      婚姻状况      受教育程度 是否有违约记录  每年账户平均余额 是否有住房贷款  \\\n",
       "编号                                                                        \n",
       "11955  84        retired   married  secondary      no   81204.0      no   \n",
       "12292  84        retired   married  secondary      no   81204.0      no   \n",
       "17123  52    blue-collar   married    primary      no   66653.0      no   \n",
       "19058  43         admin.    single  secondary      no   56831.0      no   \n",
       "12148  61  self-employed  divorced   tertiary      no   52587.0      no   \n",
       "...    ..            ...       ...        ...     ...       ...     ...   \n",
       "14925  49     management  divorced   tertiary      no   -2049.0     yes   \n",
       "15303  51     management  divorced   tertiary      no   -2282.0     yes   \n",
       "19557  52     management   married   tertiary      no   -2712.0     yes   \n",
       "10437  39  self-employed   married   tertiary      no   -3058.0     yes   \n",
       "15712  49     management   married   tertiary     yes   -6847.0      no   \n",
       "\n",
       "      是否有个人贷款       联系途径  最后一次联系时间（日） 最后一次联系时间（月）  最后一次联系交流时长  与该客户交流过的次数  \\\n",
       "编号                                                                          \n",
       "11955      no  telephone         28.0         dec       679.0         1.0   \n",
       "12292      no  telephone          1.0         apr       390.0         1.0   \n",
       "17123      no   cellular         14.0         aug       109.0         3.0   \n",
       "19058      no    unknown         15.0         may       243.0         1.0   \n",
       "12148      no   cellular         15.0         feb       394.0         3.0   \n",
       "...       ...        ...          ...         ...         ...         ...   \n",
       "14925      no    unknown         30.0         may       169.0         3.0   \n",
       "15303     yes   cellular         14.0         jul       301.0         6.0   \n",
       "19557     yes   cellular          2.0         apr       253.0         1.0   \n",
       "10437     yes   cellular         17.0         apr       882.0         3.0   \n",
       "15712     yes   cellular         21.0         jul       206.0         1.0   \n",
       "\n",
       "       距离上次联系客户后过了多久  之前与客户交流过的次数  上次活动的结果 预测客户是否会订购定期存款业务  \n",
       "编号                                                          \n",
       "11955          313.0          2.0    other             yes  \n",
       "12292           94.0          3.0  success             yes  \n",
       "17123           -1.0          0.0  unknown              no  \n",
       "19058           -1.0          0.0  unknown              no  \n",
       "12148          189.0          1.0  success             yes  \n",
       "...              ...          ...      ...             ...  \n",
       "14925           -1.0          0.0  unknown              no  \n",
       "15303           -1.0          0.0  unknown              no  \n",
       "19557           -1.0          0.0  unknown              no  \n",
       "10437           -1.0          0.0  unknown             yes  \n",
       "15712           -1.0          0.0  unknown              no  \n",
       "\n",
       "[9999 rows x 17 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.sort_values(by='每年账户平均余额', ascending=False)\n",
    "\n",
    "# data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（7）请同学结合分组和排序函数，使用一行代码，按照客户'受教育程度'分组并显示各变量的均值，同时按照'每年账户平均余额'进行降序排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年龄</th>\n",
       "      <th>每年账户平均余额</th>\n",
       "      <th>最后一次联系时间（日）</th>\n",
       "      <th>最后一次联系交流时长</th>\n",
       "      <th>与该客户交流过的次数</th>\n",
       "      <th>距离上次联系客户后过了多久</th>\n",
       "      <th>之前与客户交流过的次数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>受教育程度</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tertiary</th>\n",
       "      <td>39.522145</td>\n",
       "      <td>1851.110274</td>\n",
       "      <td>15.713468</td>\n",
       "      <td>321.332932</td>\n",
       "      <td>2.426936</td>\n",
       "      <td>58.417897</td>\n",
       "      <td>1.015065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>45.498920</td>\n",
       "      <td>1681.192225</td>\n",
       "      <td>15.889849</td>\n",
       "      <td>316.060475</td>\n",
       "      <td>2.600432</td>\n",
       "      <td>54.373650</td>\n",
       "      <td>0.758099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary</th>\n",
       "      <td>48.654709</td>\n",
       "      <td>1555.060538</td>\n",
       "      <td>15.359492</td>\n",
       "      <td>325.962631</td>\n",
       "      <td>2.651719</td>\n",
       "      <td>44.578475</td>\n",
       "      <td>0.767564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>40.143267</td>\n",
       "      <td>1308.714081</td>\n",
       "      <td>15.406026</td>\n",
       "      <td>322.195737</td>\n",
       "      <td>2.427547</td>\n",
       "      <td>59.447018</td>\n",
       "      <td>0.909408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  年龄     每年账户平均余额  最后一次联系时间（日）  最后一次联系交流时长  与该客户交流过的次数  \\\n",
       "受教育程度                                                                    \n",
       "tertiary   39.522145  1851.110274    15.713468  321.332932    2.426936   \n",
       "unknown    45.498920  1681.192225    15.889849  316.060475    2.600432   \n",
       "primary    48.654709  1555.060538    15.359492  325.962631    2.651719   \n",
       "secondary  40.143267  1308.714081    15.406026  322.195737    2.427547   \n",
       "\n",
       "           距离上次联系客户后过了多久  之前与客户交流过的次数  \n",
       "受教育程度                                  \n",
       "tertiary       58.417897     1.015065  \n",
       "unknown        54.373650     0.758099  \n",
       "primary        44.578475     0.767564  \n",
       "secondary      59.447018     0.909408  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考答案#\n",
    "\n",
    "data.groupby('受教育程度').mean().sort_values('每年账户平均余额', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=#A64646 size=2> **同学们真棒，看来大家已经掌握本节课的数据分析方法啦！**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=#A64646 size=2> **继续加油喔，快和小伙伴一起开启明天的课程学习吧！**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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